Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
FreeUnifying Transformers and SSMs through structured state space duality
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About Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
This paper presents a theoretical framework called State Space Duality (SSD) that unifies Transformers and state-space models (SSMs) such as Mamba. It shows that these model families are closely related through structured semiseparable matrices. The SSD framework leads to Mamba-2, a refinement of Mamba's selective SSM that is 2-8x faster while remaining competitive with Transformers on language modeling tasks. The paper was presented at ICML 2024.
Key Features
Theoretical connections between Transformers and state-space models via structured semiseparable matrices
State Space Duality (SSD) framework for unified understanding
Introduction of Mamba-2 architecture with 2-8x speed improvement over Mamba
Competitive performance with Transformers on language modeling benchmarks
Published at ICML 2024
Pros & Cons
Pros
- Provides a unified theoretical framework bridging two major model families
- Mamba-2 achieves significant speed improvements (2-8x) over Mamba
- Maintains competitive performance with Transformers on language modeling
- Open access paper with full code and resources
Cons
- Results demonstrated only at small to medium scale; large-scale performance not yet confirmed
- Primarily a research contribution; practical implementation may require further engineering
- Comparison is limited to language modeling tasks only
Best For
Language modeling research and developmentDesigning efficient deep learning architecturesUnderstanding theoretical links between attention and state-space modelsBuilding faster alternatives to Transformers for sequence modeling
FAQ
What is the main contribution of this paper?
The paper introduces the State Space Duality (SSD) framework, showing that Transformers and state-space models are closely related through structured semiseparable matrices. It uses this framework to design Mamba-2, a faster and refined selective SSM architecture.
How does Mamba-2 compare to Mamba?
Mamba-2 is 2-8 times faster than Mamba while maintaining competitive performance with Transformers on language modeling tasks.